import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
os.getcwd()
'C:\\Users\\delld'
os.chdir('D:\\eda projects')
data=pd.read_csv("Shark Tank India Dataset.csv")
data.head()
| episode_number | pitch_number | brand_name | idea | deal | pitcher_ask_amount | ask_equity | ask_valuation | deal_amount | deal_equity | ... | ashneer_deal | anupam_deal | aman_deal | namita_deal | vineeta_deal | peyush_deal | ghazal_deal | total_sharks_invested | amount_per_shark | equity_per_shark | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 1 | BluePine Industries | Frozen Momos | 1 | 50.0 | 5.0 | 1000.00 | 75.0 | 16.00 | ... | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 3 | 25.0 | 5.333333 |
| 1 | 1 | 2 | Booz scooters | Renting e-bike for mobility in private spaces | 1 | 40.0 | 15.0 | 266.67 | 40.0 | 50.00 | ... | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 2 | 20.0 | 25.000000 |
| 2 | 1 | 3 | Heart up my Sleeves | Detachable Sleeves | 1 | 25.0 | 10.0 | 250.00 | 25.0 | 30.00 | ... | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 2 | 12.5 | 15.000000 |
| 3 | 2 | 4 | Tagz Foods | Healthy Potato Chips | 1 | 70.0 | 1.0 | 7000.00 | 70.0 | 2.75 | ... | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 70.0 | 2.750000 |
| 4 | 2 | 5 | Head and Heart | Brain Development Course | 0 | 50.0 | 5.0 | 1000.00 | 0.0 | 0.00 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.0 | 0.000000 |
5 rows × 28 columns
data.shape
(117, 28)
data.describe
<bound method NDFrame.describe of episode_number pitch_number brand_name \
0 1 1 BluePine Industries
1 1 2 Booz scooters
2 1 3 Heart up my Sleeves
3 2 4 Tagz Foods
4 2 5 Head and Heart
.. ... ... ...
112 34 113 Green Protein
113 34 114 On2Cook
114 35 115 Jain Shikanji
115 35 116 Woloo
116 35 117 Elcare India
idea deal pitcher_ask_amount \
0 Frozen Momos 1 50.0
1 Renting e-bike for mobility in private spaces 1 40.0
2 Detachable Sleeves 1 25.0
3 Healthy Potato Chips 1 70.0
4 Brain Development Course 0 50.0
.. ... ... ...
112 Plant-Based Protein 0 60.0
113 Fastest Cooking Device 0 100.0
114 Lemonade 1 40.0
115 Washroom Finder 0 50.0
116 Carenting for Elders 0 100.0
ask_equity ask_valuation deal_amount deal_equity ... ashneer_deal \
0 5.0 1000.00 75.0 16.00 ... 1
1 15.0 266.67 40.0 50.00 ... 1
2 10.0 250.00 25.0 30.00 ... 0
3 1.0 7000.00 70.0 2.75 ... 1
4 5.0 1000.00 0.0 0.00 ... 0
.. ... ... ... ... ... ...
112 2.0 3000.00 0.0 0.00 ... 0
113 1.0 10000.00 0.0 0.00 ... 0
114 8.0 500.00 40.0 30.00 ... 1
115 4.0 1250.00 0.0 0.00 ... 0
116 2.5 4000.00 0.0 0.00 ... 0
anupam_deal aman_deal namita_deal vineeta_deal peyush_deal \
0 0 1 0 1 0
1 0 0 0 1 0
2 1 0 0 1 0
3 0 0 0 0 0
4 0 0 0 0 0
.. ... ... ... ... ...
112 0 0 0 0 0
113 0 0 0 0 0
114 1 1 0 1 0
115 0 0 0 0 0
116 0 0 0 0 0
ghazal_deal total_sharks_invested amount_per_shark equity_per_shark
0 0 3 25.0 5.333333
1 0 2 20.0 25.000000
2 0 2 12.5 15.000000
3 0 1 70.0 2.750000
4 0 0 0.0 0.000000
.. ... ... ... ...
112 0 0 0.0 0.000000
113 0 0 0.0 0.000000
114 0 4 10.0 7.500000
115 0 0 0.0 0.000000
116 0 0 0.0 0.000000
[117 rows x 28 columns]>
data.isnull().sum()
episode_number 0 pitch_number 0 brand_name 0 idea 0 deal 0 pitcher_ask_amount 0 ask_equity 0 ask_valuation 0 deal_amount 0 deal_equity 0 deal_valuation 0 ashneer_present 0 anupam_present 0 aman_present 0 namita_present 0 vineeta_present 0 peyush_present 0 ghazal_present 0 ashneer_deal 0 anupam_deal 0 aman_deal 0 namita_deal 0 vineeta_deal 0 peyush_deal 0 ghazal_deal 0 total_sharks_invested 0 amount_per_shark 0 equity_per_shark 0 dtype: int64
#total deal occur in this session
data['deal'].value_counts()
1 65 0 52 Name: deal, dtype: int64
#Five shark invested deals
All_sharks=data[data["total_sharks_invested"]==5]
All_sharks
| episode_number | pitch_number | brand_name | idea | deal | pitcher_ask_amount | ask_equity | ask_valuation | deal_amount | deal_equity | ... | ashneer_deal | anupam_deal | aman_deal | namita_deal | vineeta_deal | peyush_deal | ghazal_deal | total_sharks_invested | amount_per_shark | equity_per_shark | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 15 | 6 | 16 | Skippi Pops | Ice-Pops | 1 | 45.0 | 5.0 | 900.0 | 100.0 | 15.0 | ... | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 5 | 20.0 | 3.0 |
| 49 | 17 | 50 | Find Your Kicks India | Sneaker Resale | 1 | 50.0 | 10.0 | 500.0 | 50.0 | 25.0 | ... | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 5 | 10.0 | 5.0 |
| 63 | 20 | 64 | IN A CAN | Can Cocktails | 1 | 50.0 | 2.0 | 2500.0 | 100.0 | 10.0 | ... | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 5 | 20.0 | 2.0 |
| 79 | 25 | 80 | Sunfox Technologies | Portable ECG Device | 1 | 100.0 | 2.0 | 5000.0 | 100.0 | 6.0 | ... | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 5 | 20.0 | 1.2 |
4 rows × 28 columns
import plotly.express as px
All_sharks=data[data["total_sharks_invested"]==5]
figure=px.bar(All_sharks,x='brand_name',y='deal_amount',title='total investment & deal brands',text_auto=True,color='pitcher_ask_amount',template="plotly_dark")
figure.show()
#highest pitch ask amount
high=data[data['pitcher_ask_amount']>=100]
high.shape
(23, 28)
figure1=px.bar(high,x='brand_name',y='pitcher_ask_amount',color='deal_amount',title='high_pitcher_ask_amount',height=400,template='plotly_dark',text_auto=True)
figure1.show()
#Ask equity and deal euity of highest pitcher_aks_amount_Brand
figure2=px.bar(high,x='brand_name',y='ask_equity',color='deal_equity',title='Ask equity & Deal_equity of highest picher brand',height=500)
figure2.show()
#Least pitch ask amount
low=data[data['pitcher_ask_amount']<=25][0:10]
low.shape
(7, 28)
figure4=px.bar(low,x='brand_name',y='pitcher_ask_amount',color='deal_amount',template='plotly_dark',title='Least Pitcher ask amt')
figure4.show()
#total no.of deals
sharks=['Ashneer','Anupam','Namita','Vineeta','Aman','Peyush','Ghazal']
total_deal_sharks=[data.ashneer_deal.sum(),data.anupam_deal.sum(),data.namita_deal.sum(),data.vineeta_deal.sum(),data.aman_deal.sum(),data.peyush_deal.sum(),data.ghazal_deal.sum()]
figure5=px.bar(All_sharks,x=sharks,y=total_deal_sharks,title='Deal done by each shark',color=sharks,template='plotly_dark')
figure5.show()